Adversarial Loss: In order to make the reconstructed SR image more realistic, Generative Adversarial Networks (GANs [1]}) have been proposed and introduced into various computer vision tasks. Specifically, GAN is composed of a generator and a discriminator. The generator is responsible for generating fake samples, and the discriminator is used to determine the authenticity of the generated samples. For example, the discriminative loss function based on cross-entropy is proposed by SRGAN [2]}:
\(\mathcal {L}_{Adversarial}(I_{x},G, D) = \sum _{n=1}^{N} -logD(G(I_{x})),\) 
